Radiologists use radiographic images such as mammograms to detect and pinpoint suspicious lesions in a patient as early as possible, e.g., before a disease is readily detectable by other, intrusive methods. As such, there is real benefit to the radiologist being able to locate, based on imagery, extremely small cancerous lesions and precursors. Microcalcifications, particularly those occurring in certain types of clusters, are one signature of concern. Although the individual calcifications tend to readily absorb radiation and can thus appear quite bright in a radiographic image, various factors including extremely small size, occlusion by other natural structure, appearance in a structurally “busy” portion of the image, all sometimes coupled with radiologist fatigue, may make some calcifications hard to detect upon visual inspection.
Computer-Aided Detection (CAD) algorithms have been developed to assist radiologists in locating potential lesions in a radiographic image. CAD algorithms operate within a computer on a digital representation of the mammogram set for a patient. The digital representation can be the original or processed sensor data, when the mammograms are captured by a digital sensor, or a scanned version of a traditional film-based mammogram set. An “image,” as used herein, is assumed to be at least two-dimensional data in a suitable digital representation for presentation to CAD algorithms, without distinction to the capture mechanism originally used to capture patient information. The CAD algorithms search the image for objects matching a signature of interest, and alert the radiologist when a signature of interest is found.
One signature of interest is a microcalcification. Existing CAD algorithms use various strategies to locate potential microcalcifications. In U.S. Pat. No. 6,014,452, all pixels having an intensity above a global fixed threshold are used as seed locations for potential microcalcifications. U.S. Pat. No. 6,801,645 applies a difference of Gaussians filter to enhance microcalcifications, and then thresholds. U.S. Pat. No. 7,593,561 applies a fixed filter that enhances contrast at an image location when a central 3×3 pixel region is brighter than pixel rings three and six pixels from the image location, and then adaptively and iteratively thresholds the adjusted image to obtain a desired number of clusters.
Another signature of interest is a microcalcification cluster. Existing CAD algorithms use various strategies to label potential microcalcification clusters as suspicious, including trained neural networks and feature-weighted linear discriminants, as demonstrated in U.S. Pat. No. 7,593,561.
Classification of anomalies may be performed using a probability density function (PDF) that describes the relative likelihood of observing any given sample value of a random variable. The integral of a PDF over all possible values is 1; the integral of a PDF over a subset of the random variable's range expresses the probability that a drawn sample of a random variable will fall within that range.
PDFs that can be expressed by a closed-form equation are generally well understood, and many applications for such PDFs have been developed. On the other hand, the practical estimation of a PDF for a complex multidimensional random variable, particularly one with an unknown and possibly irregular distribution in each dimension, and/or long, sparsely populated tails, has in large part eluded researchers. In the area of pattern and image recognition, for instance, many researchers have abandoned PDF approaches and concentrated on known solvable alternatives, such as Neural Networks and linear discriminant functions, due to the practical difficulties in applying a PDF approach.